Open AccessThe creation of a regression model of the Earth’s pole motion with a feature of dynamic prediction
Author(s) -
A N Khairutdinova,
Regina Mubarakshina,
Alexey Andreev,
Yury Nefedyev,
Natalya Demina
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1697/1/012029
Subject(s) - position (finance) , regression , motion (physics) , harmonics , series (stratigraphy) , computer science , feature (linguistics) , regression analysis , set (abstract data type) , dynamics (music) , mathematics , control theory (sociology) , artificial intelligence , machine learning , geology , statistics , physics , paleontology , linguistics , philosophy , finance , quantum mechanics , voltage , acoustics , economics , programming language , control (management)
This work is dedicated to the modern and relevant problem of predicting the Earth’s pole motion. Using regression modelling, we form a complex model, consisting of a set of optimal mathematical structures each describing the dependence of its step’s remnant on time. The comparison between the results produced in this paper with other works on the study of North pole dynamics has shown that the models obtained using adaptive regression modelling (ARM) approach allows predicting the Y-coordinate more accurately while conserving the accuracy of the X-coordinate. Our results confirm the promise of using the so called adaptive dynamic regressions developed currently for describing the Earth’s pole position’s dynamics. The ARM-approach compared to the classic methods for analyzing time series has a number of advantages: 1) an expansion of the concept of a mathematical model’s structure describing a certain dynamics could be performed; 2) the oscillations’ harmonics stable in time are isolated; 3) the accuracy of predicting changes over a certain time period increases several times, which has an important practical value.